Update README.md

README.md

@@ -1,39 +1,33 @@
----
-license: apache-2.0
----
-# MyTextGen Model
+# MyQA Model
 
-This model is a GPT-2 based model designed for text generation tasks.
+This model is designed for question answering tasks based on provided text documents.
 
 ## Model Description
 
-This model is based on the GPT-2 architecture and trained on a diverse dataset of text from various sources, including literature, articles, and online content. It can generate coherent and contextually relevant text based on the input provided.
+This model can analyze the contents of a text document and generate answers to questions posed by the user. It is built on the [base model type, e.g., BERT, RoBERTa, etc.] architecture and is fine-tuned for the task of question answering.
 
 ## Intended Use
-- **Task Type**: Text Generation
-- **Use Cases**: 
-  - Generating creative writing (stories, poems, etc.)
-  - Creating conversational agents
-  - Responding to prompts in various contexts
-  - Summarizing information and more
+- **Task Type**: Question Answering
+- **Use Cases**:
+  - Answering questions based on the content of documents.
+  - Assisting with information retrieval from text sources.
+  - Providing summaries or key information extracted from documents.
 
 ## How to Use
 You can use this model with the Hugging Face Transformers library as follows:
 
 ```python
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
+from transformers import pipeline
 
-# Load the model and tokenizer
-model = GPT2LMHeadModel.from_pretrained("username/mytextgen")  # Replace with your model path
-tokenizer = GPT2Tokenizer.from_pretrained("username/mytextgen")  # Replace with your model path
+# Load the question-answering pipeline
+qa_pipeline = pipeline("question-answering", model="username/myqa")  # Replace with your model path
 
-# Prepare input text
-input_text = "Once upon a time"  # Your input prompt
-inputs = tokenizer(input_text, return_tensors="pt")
+# Example document
+context = """Your text document content here."""
+question = "What is the main topic of the document?"
 
-# Generate text
-outputs = model.generate(**inputs, max_length=100, num_return_sequences=1, temperature=0.7)
+# Generate answer
+result = qa_pipeline(question=question, context=context)
 
-# Decode and print the generated text
-generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(generated_text)
+# Print the answer
+print(result['answer'])